Table of Contents

• Statistically Evaluating Social Media Sentiment Trends towards COVID-19 Non-Pharmaceutical Interventions with Event StudiesJingcheng Niu, Erin E. Rees, Victoria Ng, Gerald Penn. 1-6 [doi]
• View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated DataPayam Karisani, Jinho D. Choi, Li Xiong 0001. 7-12 [doi]
• The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corporaAntonio Miranda-Escalada, Eulàlia Farré-Maduell, Salvador Lima-López, Luis Gascó, Vicent Brivá-Iglesias, Marvin Agüero-Torales, Martin Krallinger. 13-20 [doi]
• Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021Arjun Magge, Ari Z. Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulàlia Farré, Salvador Lima-López, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M. Banda, Martin Krallinger, Graciela Gonzalez-Hernandez. 21-32 [doi]
• BERT based Transformers lead the way in Extraction of Health Information from Social MediaSidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma. 33-38 [doi]
• KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug EffectsAndrey Sakhovskiy, Zulfat Miftahutdinov, Elena Tutubalina. 39-43 [doi]
• Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in TweetsGeorge-Andrei Dima, Dumitru-Clementin Cercel, Mihai Dascalu. 44-51 [doi]
• Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health ApplicationsYuting Guo, Yao Ge, Mohammed Ali Al-garadi, Abeed Sarker. 52-57 [doi]
• BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on TwitterAlham Fikri Aji, Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Radityo Eko Prasojo, Tirana Fatyanosa. 58-64 [doi]
• UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter postsAlberto Valdes, Jesus Lopez, Manuel Montes. 65-68 [doi]
• System description for ProfNER - SMMH: Optimized finetuning of a pretrained transformer and word vectorsDavid Carreto Fidalgo, Daniel Vila-Suero, Francisco Aranda Montes, Ignacio Talavera. 69-73 [doi]
• Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social MediaSergio Santamaria Carrasco, Roberto Cuervo Rosillo. 74-76 [doi]
• Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared TasksTong Zhou, Zhucong Li, Zhen Gan, Baoli Zhang, Yubo Chen 0001, Kun Niu, Jing Wan, Kang Liu 0001, Jun Zhao 0001, Yafei Shi, Weifeng Chong, Shengping Liu. 77-82 [doi]
• Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021Usama Yaseen, Stefan Langer. 83-87 [doi]
• BERT based Adverse Drug Effect Tweet ClassificationTanay Kayastha, Pranjal Gupta, Pushpak Bhattacharyya. 88-90 [doi]
• A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for SMM4H 2021Mohab Elkaref, Lamiece Hassan. 91-94 [doi]
• Text Augmentation Techniques in Drug Adverse Effect Detection TaskPavel Blinov. 95-97 [doi]
• Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa TransformersLung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, Kuo-Kai Shyu. 98-101 [doi]
• NLP@NISER: Classification of COVID19 tweets containing symptomsDeepak Kumar, Nalin Kumar, Subhankar Mishra. 102-104 [doi]
• Identification of profession & occupation in Health-related Social Media using tweets in SpanishVictoria Pachón, Jacinto Mata Vázquez, Juan Luis Domínguez-Olmedo. 105-107 [doi]
• Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary ClassificationPedro Ruas, Vitor D. T. Andrade, Francisco M. Couto. 108-111 [doi]
• Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuningAdarsh Kumar, Ojasv Kamal, Susmita Mazumdar. 112-114 [doi]
• UoB at ProfNER 2021: Data Augmentation for Classification Using Machine TranslationFrances Adriana Laureano De Leon, Harish Tayyar Madabushi, Mark Lee 0001. 115-117 [doi]
• IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification on Health-Related Imbalanced Twitter DatasetsVarad Pimpalkhute, Prajwal Nakhate, Tausif Diwan. 118-122 [doi]
• OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language modelsYing Luo, Lis Pereira, Ichiro Kobayashi. 123-125 [doi]
• PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in TweetsZongcheng Ji, Tian Xia 0004, Mei Han. 126-127 [doi]
• Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social mediaVasile Pais, Maria Mitrofan. 128-130 [doi]
• Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in TweetsMax Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak. 131-134 [doi]
• Classification of COVID19 tweets using Machine Learning ApproachesAnupam Mondal, Sainik Kumar Mahata, Monalisa Dey, Dipankar Das 0001. 135-137 [doi]
• Fine-tuning BERT to classify COVID19 tweets containing symptomsRajarshi Roychoudhury, Sudip Kumar Naskar. 138-140 [doi]
• Identifying professions & occupations in Health-related Social Media using Natural Language ProcessingJosé-Alberto Mesa-Murgado, Ana Belén Parras Portillo, Pilar López-Úbeda, María-Teresa Martín Valdivia, Luis Alfonso Ureña López. 141-145 [doi]
• Approaching SMM4H with auto-regressive language models and back-translationJoseph Cornelius, Tilia Ellendorff, Fabio Rinaldi 0001. 146-148 [doi]
• ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021Atul kr. Ojha, Priya Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. McCrae. 149-152 [doi]